*********TECHNO INDIA COLLEGE OF TECHNOLOGY********** RAJARHAT,KOLKATA - 700156 A full powerpoint presentation on big data analytics and Hadoop. This is made by: SK IBRAHIM ANAM , SOUVIK JANA. Designed by: SK IBRAHIM ANAM Group Members: SOUVIK JANA. SK IBRAHIM ANAM. VISHAL KUMAR.

2. WHAT IS
Data is raw, unorganized facts that need to be
processed. Data can be something simple,
seemingly random and of itself worthless useless
until it is organized.

3. DIFFERENT TYPES OF DATA
Traditional RDBMS deals with
only Structured Data
Need of a Technology which deals with
Semi – Structured Data ,Unstructured
Data and Structured Data as well
Semi-Structured Data

4. Traditional Concept of Data Storage
Organizations
Banking Sector
Stock Exchange
Hospital
Social Media
Online Shopping
Others
Extract Data Transform Data
Load into
DataBase
End Users Generate
Reports & Perform
Analytics
Managing Data
Processing Data
Data GrowsDifficult

5. Drawback of Using Traditional Approach
Expensive Time Consuming Scalability
Storage Size Resource Failure

6. The Model of Generating or Consuming Data
Has Change...
OLD MODEL - Few companies are generating the data, all
other consuming the data.
NEW MODEL -All of us generating the data, and all of us
consuming the data.

7. BIG DATA

8. WHAT IS
Big data means really a Big Data, it is a
collection of large datasets that cannot be
processed using traditional computing
techniques. It requires new architecture , new
techniques , various tools and frameworks .

9. Definition of BIG DATA

10. Different Sources of DATA
SOURCES

11. WHERE THE BIG DATA IS USED
IT Industries
Manufacturing Industries
Telecommunications
Banking sector
Healthcare

12. CHALLENGES IN HANDLING BIG DATA
There are two main challenges in handle BIG DATA
1. How do we store and manage such a huge volume
of DATA, efficiently.
2. How do we process & extract valuable information
from the huge volume of DATA within a given
time frame.

13. BRIEF HISTORY OF HADOOP

14. WHAT IS
Hadoop is a open Source Framework. It is designed to
store and Process huge volume of Data, efficiently.
Hadoop is a platform that provides both distributed
storage and computational capabilities.

15. Why HADOOP Is Used

16. MAJOR COMPONENT S OF HADOOP ECOSYSTEM
HADOOP
COMPONENTS
HADOOP
DISTRIBUTED
FILE SYSTEM
Google
MAPREDUCE
ALGORITHM
Storage Processing

17. HADOOP ECOSYSTEM
Flume Sqoop
Semi-Structured or
Unstructured Data
Structured Data
Import or Export

18. Features of Hadoop
Cost Effective System (Use Commodity Machine)
Large Cluster of Nodes (Processing Power & Storage
Capacity is Increase)

19. Features of Hadoop
Parallel Processing (Less Time is Required to Store &
Access the Data)
Distributed Data (Data is Distributed in Different Nodes)

20. Features of Hadoop
Automatic Failover Management
Heterogeneous Cluster

21. Features of Hadoop
Scalability

22. How The Data Is Stored In Hadoop Clusters
Rack 1 Rack 2
Node 1 Node 4Node 3Node 2

23. Hadoop Distributed File System
Name Node
Task Tracker
Client
Block5 Block2 Block4
Block3 Block1 Block6
Data Node
Block4 Block1 Block3
Block2 Block6 Block5
Block1 Block2 Block3
Block4 Block5 Block6
Data Node Data Node
Task Tracker Task Tracker
Job Tracker
Secondary
Name Node

24. MapReduce Flow

25. MapReduce Framework
Map Reduce works by breaking the processing into two phases
Map Phase & Reduce Phase
Input Split Map Reduce Output
Shuffle
& Sort

26. Disadvantages
Security
Concerns
Not Fit For
Small Data

27. Future Scope of Big Data & Hadoop

28. Conclusion...

29. Source of Information
Google

30. Presented By
Vishal Kumar
Sk Ibrahim Anam
Souvik Jana

31. Thank You